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Sunday, October 14, 2012

When Pigs Fly Daily Report S&P 500 Emini Futures 11th Oct 2012

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When Pigs Fly Daily Report S&P 500 Emini Futures 11th Oct 2012.Have a look at the description for flying pigs below and after you finally believe yes program trading controls the markets sign up for a free trial of our bot tracking software sceeto. You will win more of your trades if you can track the bots properly. Sign up at http://www.sceeto.com Please also check out our free binary options signals at http://www.binaryforecast.com
"When pigs fly" is an adynaton, a way of saying that something will never happen. The phrase is often used for humorous effect, to scoff at over-ambition. There are numerous variations on the theme; when an individual with a reputation for failure finally succeeds, onlookers may sarcastically claim to see a flying pig. ("Hey look! A flying pig!") [1] Other variations on the phrase include "And pigs will fly," this one in retort to an outlandish statement.
text courtesy of Wikipedia
Computerization of the order flow in financial markets began in the early 1970s, with some landmarks being the introduction of the New York Stock Exchange's “designated order turnaround” system (DOT, and later SuperDOT), which routed orders electronically to the proper trading post, which executed them manually. The "opening automated reporting system" (OARS) aided the specialist in determining the market clearing opening price (SOR; Smart Order Routing).
Program trading is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over US$1 million total. In practice this means that all program trades are entered with the aid of a computer. In the 1980s program trading became widely used in trading between the S&P500 equity and futures markets.
In stock index arbitrage a trader buys (or sells) a stock index futures contract such as the S&P 500 futures and sells (or buys) a portfolio of up to 500 stocks (can be a much smaller representative subset) at the NYSE matched against the futures trade. The program trade at the NYSE would be pre-programmed into a computer to enter the order automatically into the NYSE’s electronic order routing system at a time when the futures price and the stock index were far enough apart to make a profit.
At about the same time portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer model based on the Black–Scholes option pricing model.
Both strategies, often simply lumped together as "program trading", were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987 stock market crash. Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.[21]
Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., decimalization, which changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share, may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers' trading advantage, thus increasing market liquidity.
This increased market liquidity led to institutional traders splitting up orders according to computer algorithms so they could execute orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time-weighted average price or more usually by the volume-weighted average price.
A further encouragement for the adoption of algorithmic trading in the financial markets came in 2001 when a team of IBM researchers published a paper[22] at the International Joint Conference on Artificial Intelligence where they showed that in experimental laboratory versions of the electronic auctions used in the financial markets, two algorithmic strategies (IBM's own MGD, and Hewlett-Packard's ZIP) could consistently out-perform human traders. MGD was a modified version of the "GD" algorithm invented by Steven Gjerstad & John Dickhaut in 1996/7;[23] the ZIP algorithm had been invented at HP by Dave Cliff (professor) in 1996.[24] In their paper, the IBM team wrote that the financial impact of their results showing MGD and ZIP outperforming human traders "...might be measured in billions of dollars annually"; the IBM paper generated international media coverage.
As more electronic markets opened, other algorithmic trading strategies were introduced. These strategies are more easily implemented by computers, because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously.
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